Informational inefficiency on bitcoin futures

This paper investigates the dynamics and drivers of informational inefficiency in the Bitcoin futures market. To quantify the adaptive pattern of informational inefficiency, we leverage two groups of statistics which measure long memory and fractal dimension to construct a global-local market inefficiency index. Our findings validate the adaptive market hypothesis, and the global and local inefficiency exhibits different patterns and contributions. Regarding the driving factors of the time-varying inefficiency, our results suggest that trading activity of retailers (hedgers) increases (decreases) informational inefficiency. Compared to hedgers and retailers, the role played by speculators is more likely to be affected by the COVID-19 crisis. Extremely bullish and bearish investor sentiment has more significant impact on the local inefficiency. Arbitrage potential, funding liquidity, and the pandemic exert impacts on the global and local inefficiency differently. No significant evidence is found for market liquidity and policy uncertainty related to cryptocurrency.

ASIAN BIOTECH CLINICAL TRIAL DEMAND MOVES TO THE WEST

The critical element, of course, is that there can be no compromise on trial design, execution and data collection to deliver a robust drug development package. AUM's strategy is to reverse this flow and go from Asia to the West, satisfying the need of the Asian population for innovative and affordable drug development and reducing the cost of health care in Western countries by introducing drugs at an "Asian" price point. [...]the dislocation in services from widespread lockdowns resulted in significant delays in data collection and management of research samples. Even when we could transfer them to appropriate laboratories for analysis, the staff shortages, backlog of samples, and supply chain disruption of critical reagents and parts caused troubling delays in obtaining and analyzing data.

Clean Energy Action Index Efficiency: An Analysis in Global Uncertainty Contexts

Climate change, the scarcity of fossil fuels, advances in clean energy, and volatility of crude oil prices have led to the recognition of clean energy as a viable alternative to dirty energy. This paper investigates the multifractal scaling behavior and efficiency of green finance markets, as well as traditional markets such as gold, crude oil, and natural gas between 1 January 2018, and 9 March 2023. To test the serial dependency (autocorrelation) and the efficient market hypothesis, in its weak form, we employed the Lo and Mackinlay test and the DFA method. The empirical findings showed that returns data series exhibit signs of (in)efficiency. Additionally, there is a negative autocorrelation among the crude oil market, the Clean Energy Fuels Index, the Global Clean Energy Index, the gold market, and the natural gas market. Arbitration strategies can be used to obtain abnormal returns, but caution should be exercised as prices may increase above their actual market value and reduce the profitability of trading. This work contributes to the body of knowledge on sustainable finance by teaching investors how to use predictive strategies on the future values of their investments.

A Novel Machine Learning-assisted Pairs Trading Approach for Trading Risk Reduction

The cryptocurrency market has been growing rapidly in recent years. The volume of transactions and the number of participants in the cryptocurrency market makes it huge enough that we cannot ignore it. At the same time, the global stock market has also reached a new height in the past two years. However, due to the COVID epidemic and other political and economic-related factors in the last two years, the uncertainty in the capital market remains high, and short-term large fluctuations occur frequently;thus, many investors have suffered substantial losses. Pairs trading, an advanced statistical arbitrage method, is believed to hedge the risk and profit off the market regardless of market condition. Amongst the vast literature on pairs trading, there have been investors trading a pair of cryptocurrencies or a pair of stocks using machine learning or empirical methods. This research probes the boundary of utilizing machine learning methods to do pairs trading with one stock asset and another cryptocurrency. Briefly, we built an assets pool with both stocks and cryptocurrencies to find the best trading pair. In addition, we applied mainstream machine learning models to the trading strategy. We finally evaluated the accuracy of the proposed method in prediction and compared their returns based on the actual U.S. Stock and Cryptocurrency Market data. The test results show that our method outperforms other state-of-the-art methods. © 2022 IEEE.

Analyze the Effects of COVID-19 on Energy Storage Systems: A Techno-Economic Approach

During the COVID-19 pandemic, the U.S. power sector witnessed remarkable electricity demand changes in many geographical regions. These changes were evident in population-dense cities. This paper incorporates a techno-economic analysis of energy storage systems (ESSs) to investigate the pandemic's influence on ESS development. In particular, we employ a linear program-based revenue maximization model to capture the revenues of ESS from participating in the electricity market, by performing arbitrage on the energy trading, and regulation market, by providing regulation services to stabilize the grid's frequency. We consider five dominant energy storage technologies in the U.S., namely, Lithium-ion, Advanced Lead Acid, Flywheel, Vanadium Redox Flow, and Lithium-Iron Phosphate storage technologies. Extensive numerical results conducted on the case of New York City (NYC) allow us to highlight the negative impact that COVID-19 had on the NYC power sector. © 2023 IEEE.

The conditional impact of market conditions, volatility and liquidity shocks on the arbitrage opportunities during pre-COVID and COVID periods

The study examines the effects of market conditions, volatility and liquidity shocks on the arbitrage profits during pre-COVID and COVID periods. The study uses a conditional quantile regression and finds no significant difference in the impact of market conditions on the arbitrage profits during pre-COVID and COVID crisis periods. The increase in volatility combined with low liquidity during the COVID period makes arbitrage non-viable. However, the decline in volatility during the COVID period encourages investors to initiate arbitrage. The results are useful to fund managers and market analysts to develop suitable trading strategies and stock market regulators to take necessary steps to improve price discovery mechanisms and market efficiency. © 2023 John Wiley & Sons Ltd.

Central bank credibility during COVID-19: Evidence from Japan

Japanese realized and expected inflation has been below the Bank of Japan's two percent target for many years. We examine the impact of announcements of expansionary monetary and fiscal policy under COVID-19 on inflation expectations from an arbitrage-free term structure model of nominal and real yields. We find that both types of policies failed to lift inflation expectations, which instead declined notably over the pandemic period and are projected to only slowly revert back to Bank of Japan target levels. Our results therefore illustrate the challenges faced in raising well-anchored low inflation expectations. © 2022

The Problem of Asymmetric Development of the Global Financial Market

The transformation of the modern global monetary and financial system involves the elimination of institutional and functional contradictions existing at various levels. Some contradictions arose as a result of the asymmetric development of the global financial market (GFM). The aim of the article is to substantiate the asymmetry of the GFM development as an organic phenomenon, which, on the one hand, becomes a serious obstacle to the functioning and progressive development of the world economy, and, on the other hand, is the driving force behind this development. The authors apply general logical, theoretical, empirical, and special research methods. The origins of the asymmetric development of the GFM are determined. Endogenous and exogenous factors of GFM asymmetry were revealed. The article considers examples of asymmetry in various GFM segments. The negative impact of the global financial and economic crisis of 2008–2009 and the coronavirus pandemic on increasing the asymmetry of the GFM development has been determined. Based on the analysis of the key macroeconomic indicators of the top 20 countries in terms of GDP, the asymmetric nature and the absence of stable patterns that determine the country's position in the world ranking are revealed. The authors conclude that the asymmetry of the GFM development is an organic phenomenon, caused by a wide range of causes of endogenous and exogenous nature. Endogenous asymmetries can be partially compensated either through complete economic isolation, which is likely to lead to a slowdown in development and lagging behind other countries in the future or through active involvement in a system of world economic relations based on fair partnerships. Exogenous asymmetry, due to the peculiarities of the historically established world order, is destructive for all participants in the global economic system, including those whose interests must be protected in the first place. © Balyuk I.A., Balyuk M.A., 2022.

Keep it complex! Prodi's curse and the EU fiscal governance regime complex

The EU fiscal framework has gradually morphed into a regional regime complex through various reforms of the preventive and corrective arms of the Stability and Growth Pact. A regime complex encourages actors to arbitrage between partially overlapping, parallel and nested rules. By drawing on this central insight, this article demonstrates that regime complexity enables member states to respect the letter but not the spirit of the fiscal rules to lower the cost of compliance. It further shows empirically how regime complexity weakens technocratic enforcement capacity when authority is dispersed across multiple levels of governance by focusing on the example of the general escape clauses during the coronavirus pandemic.

The profitability of pair trading strategy in stock markets: Evidence from Toronto stock exchange

Market practitioners and speculators attempt to make benefits from the existence of market price gaps and profit opportunities by arbitrage strategies. Although some investors trade stocks based on the available financial and fundamental information of a particular share, there are others who make profits by risk hedging and swing trading opportunities. One of these strategies is pairs trading, which is a sub‐category of statistical arbitrage. Pairs trading can assure reasonably a risk‐free profit gaining. This paper aims to make a hypothetical portfolio composed of pairs of stocks by exploring a significant association between their prices in the Toronto Stock Exchange, TSX. We compare the profitability of distance, co‐integration, and copula functions as the pair's selection and trading strategy devices in TSX over January 2017 to June 2020. Our results show that the highest profitability comes from trading by the copula method. Our time frame includes two heterogeneous pre and post COVID‐19 periods. Although the financial markets are struggling with a hard situation over the COVID‐19 days, the performance of the methodologies is not affected by the crisis.

Central bank credibility during COVID-19: Evidence from Japan

Japanese realized and expected inflation has been below the Bank of Japan's two percent target for many years. We examine the impact of announcements of expansionary monetary and fiscal policy under COVID-19 on inflation expectations from an arbitrage-free term structure model of nominal and real yields. We find that both types of policies failed to lift inflation expectations, which instead declined notably over the pandemic period and are projected to only slowly revert back to Bank of Japan target levels. Our results therefore illustrate the challenges faced in raising well-anchored low inflation expectations.

Investissement

Le ralentissement de la pratique conventionnelle du Canada occasionné par la pandémie de COVID-19 s’est poursuivi en 2021, alors qu’aucun nouveau traité n’a été conclu en matière d’investissement. Cela n’a toutefois pas empêché la publication d’un nouveau modèle d’accord de promotion et de protection des investissements étrangers (APIE) par Ottawa.1 Le phénomène de tarissement des réclamations contre le Canada s’est également poursuivi, de manière prévisible, avec l’abandon du règlement des différends entre investisseur étranger et État (RDIE) entre le Canada et les États-Unis dans l’Accord entre le Canada, les États-Unis et le Mexique (ACÉUM).2 La transition du Canada du statut d’un des États les plus visés par des réclamations à celui d’État de nationalité des investisseurs parmi les plus grands utilisateurs du RDIE est maintenant complétée, avec un nombre record de sentences arbitrales rendues en 2021 dans des affaires initiées par des investisseurs canadiens. Le nouvel APIE-type du Canada de 2021 et les développements dans le contentieux de l’investissement canadien à l’étranger font l’objet d’une analyse détaillée dans la chronique cette année. Un tour d’horizon des principaux autres faits marquants de 2021 est d’abord effectué.

Price discovery in China's crude oil futures markets: An emerging Asian benchmark?

We examine the price discovery performance of China's crude oil futures traded on the Shanghai International Energy Exchange (INE) for the spot prices of 19 types of deliverable and nondeliverable Asian crude oil. We find evidence for the INE crude oil futures price discovery function even at the early stage for almost all the deliverable crudes and some nondeliverable crudes. Both the INE crude oil futures price and the spot price significantly contribute to the price discovery process, with substantially time-varying informational roles. While the price discovery performance was severely damaged around the period of COVID-19 pandemic shock intensification in China with the temporary cancellation of nighttime trading, it improved to some extent after China started the recovery from the shock. But such improvement deteriorated drastically and disappeared since early 2021. Further analysis reveals that both economic fundamentals (e.g., the warehouse inventory) and trading-related characteristics of the futures market are significant determinants of the price discovery performance. The overall findings imply that the INE crude oil futures market has evolved into a useful and important information source in pricing Asian crudes, and is on the path to emerge as an Asian benchmark.

Intraday Price Discovery between Spot and Futures Markets of NIFTY 50: An Empirical Study during the Times of COVID-19

The current study investigates the intraday dynamics of futures and spot markets in India. By analyzing one-minute data of Nifty 50 and the associated futures index, the study finds that both the markets are cointegrated. The results of the VECM reveal that any disequilibrium between the spot and futures market is restored by the spot market. Granger causality tests reveal that the spot and futures markets have a bidirectional causal relationship. Common factor weights and Hasbrouck’s information share (IS) reveal the greater role of the futures market in price discovery. Gonzalo and Granger's common factor model indicates that the permanent factor is made up of futures series only. Using the BEK-GARCH model, we found two-way volatility spillovers between the spot and futures markets. The futures market is found to have a greater impact in terms of volatility spillovers also. The findings of our research are relevant to investors, money managers, traders, and policymakers.

How Can Lower-Income Countries Access COVID-19 Medicines Without Destroying the Patent System? The National Exhaustion Solution

Many scholars argued that improving access to medicine requires major amendments to the patent system, which is structured according to the Agreement on Trade-Related Aspects of Intellectual Property Rights. This article argues that the argument is not necessarily true. Amending Article 6 of the TRIPS Agreement to adopt a national exhaustion rule for pharmaceutical patents would be sufficient to achieve a considerable improvement in access to medicine while simultaneously strengthening patent protection. This proposal encourages the pharmaceutical industry to adopt a price discrimination policy whereby Pharma would lower medicine prices in the lower-income countries. Accordingly, global access to new medicines such as COVID-19 medicines could be increased as these countries have the majority of poor people. At the same time, Pharma can continue to sell the same medicine in higher-income countries at higher prices, generating sufficient profits to incentivize research and development. © 2022, National Institute of Science Communication and Information Resources. All rights reserved.

Hedging commodities in times of distress: The case of COVID‐19

This study examines the relation between the COVID-19 pandemic and hedge efficiency in commodities futures markets. In particular, we first evaluate the informational content of commodity futures by investigating whether futures prices are accurate and unbiased predictors of future-spot prices, and then we identify key financial and real economy transmission channels associated with the pandemic. We use data of all contracts from all commodities traded at Brazilian futures markets from 2018 to 2020. We document market inefficiency and bias for all commodities. We also find that COVID-19 has a negative correlation with hedge efficiency, and that liquidity, economic activity, export, and agriculture's employment share are transmission channels to hedge efficiency.

Time-Varying Causalities in Prices and Volatilities between the Cross-Listed Stocks in Chinese Mainland and Hong Kong Stock Markets

Due to the heterogeneity of investor structure between the Chinese mainland stock market (A-share market) and the Hong Kong stock market (H-share market) as well as the limitations on arbitrage activities, most cross-listed stocks in the two markets (AH stocks) have the characteristics of “one asset, two prices”, in which AH stocks with the same vote rights and dividend streams are traded at different prices in different markets. Based on the VAR (LA-VAR as well) model and a four-variable system including AH stock indices (AHXA, AHXH), the China Securities Index 300 (CSI 300), and the Hang Seng Index (HSI), this paper applies a new time-varying causality test to examine the causalities in prices and volatilities for two pairings (AXHA-AHXH pairing and CSI 300-HSI pairing) during the sample period spanning from 4 January 2010 to 21 May 2021. The empirical results exhibit statistically significant time-varying causalities of the two pairings. Specifically, at the price level, AHXH has a significant negative causal effect on AHXA from October 2017 to February 2020 except for several months in 2018, while AHXA merely has a negative impact on AHXA during a short period from March 2017 to May 2017. Of note, the direction of causalities in volatilities between AHXA and AHXH reverses. A positive causality is found from AHXA to AHXH at the 5% significance level during the period of April 2014 through May 2021, while no causality is detected in the opposite direction during the whole sample period. Meanwhile, the volatilities of CSI 300 significantly Granger cause those of HSI over the whole sample period, but not vice versa. Implications of our results are discussed.

Crisis and arbitrage opportunities: The role of causation, effectuation and entrepreneurial learning

This article examines the process by which entrepreneurs identify and work with an arbitrage opportunity emerging from an episodic crisis. Although prior research has investigated the role of entrepreneurial characteristics and context on opportunity development, the specific manner in which these factors emerge in the course of opportunity development during a crisis remain underexplored. By adopting a qualitative approach grounded in case studies of eight entrepreneurs in the US distillery industry, this article addresses that gap by examining the process of arbitrage opportunity development during COVID-19. Our study reveals the primacy of both causation and effectuation-based entrepreneurial decision logics and the role of double-loop learning, as entrepreneurs interact with the time-compressed duration of the arbitrage opportunity. Implications and insights for entrepreneurs, researchers and policymakers are discussed.

The COVID-19 pandemic, short-sale ban, and market efficiency: empirical evidence from the European equity markets

This study looks at the inefficiency of stock indices of France, Italy, and Spain around their financial regulatory authorities' short-sale ban during the COVID-19 pandemic crisis. The empirical analysis of this study provides evidence of price predictability of the basis of futures contract prior to the short-sale restriction. Moreover, the results show a significant underpricing in futures contracts of FTSE MIB and IBEX35 indices while the two months of short-sale banned period. These findings suggest that prohibiting short selling during the market downturn might undermine the stock markets' efficiency and generate arbitrage opportunities for speculative investors.

Predicting Volatility Index According to Technical Index and Economic Indicators on the Basis of Deep Learning Algorithm

The Volatility Index (VIX) is a real-time index that has been used as the first measure to quantify market expectations for volatility, which affects the financial market as a main actor of the overall economy that is sensitive to the environmental and social aspects of investors and companies. The VIX is calculated using option prices for the S&P 500 Index (SPX) and is expressed as a percentage. Taking into account that VIX only shows the implicit volatility of the S&P 500 for the next 30 days, the authors develop a model for a near-optimal state trying to avoid uncertainty and insufficient accuracy. The researchers are trying to make a contribution to the theory of socially responsible portfolio management. The developed approach allows potential investments to make decisions regarding such important topics as ethical investing, performance analysis, as well as sustainable investment strategies. The approach of this research allows to use deep probabilistic convolutional neural networks based on conditional variance as a linear function of errors with the aim of estimating and predicting the VIX. For this purpose, the use of technical indicators and economic indexes such as Chicago Board Options Exchange (CBOE) VIX and S&P 500 is considered. The results of estimating and predicting the VIX with the proposed method indicate high precision and create a certainty in modeling to achieve the goals.